IDF.R

# This file contains the functions:  
# -IDF.agg for the preparing the data
# -IDF.plot for plotting of IDF curves at a chosen station


#### IDF.agg ####

#' Aggregation and annual maxima for choosen durations
#' @description Aggregates several time series for chosen durations and finds annual maxima 
#' (either for the whole year or chosen months). Returns data.frame that can be used for
#' the function \code{\link{gev.d.fit}}.
#'
#' @param data list of data.frames containing time series for every station. 
#' The data.frame must have the columns 'date' and 'RR' unless onther names 
#' are specified in the parameter `names`. The column 'date' must contain strings with 
#' standard date format.
#' @param ds numeric vector of aggregation durations. 
#' (Must be multiples of time resolution at all stations.)
#' @param na.accept numeric giving maximum number of missing values for which annual max. should still be calculated
#' @param which.stations optional, subset of stations. Either numeric vector or character vector 
#' containing names of elements in data. If not given, all elements in `data` will be used.
#' @param which.mon optional, subset of months of which to calculate the annual maxima from. 
#' @param names optional, character vector of length 2, containing the names of the columns to be used. 
#' @param cl optional, number of cores to be used from \code{\link[pbapply]{pblapply}} for parallelization.
#'
#' @details If data contains stations with different time resolutions that need to be aggregated at
#' different durations, IDF.agg needs to be run seperately for the different groups of stations. 
#' Afterwards he results can be joint together using `rbind`.
#'
#' @return data.frame containing the annual intensity maxima [mm/h] in `$xdat`, the corresponding duration in `$ds` 
#' and the station id or name in `$station`.
#' 
#' @seealso \code{\link{pgev.d}}
#' 
#' @export
#' @importFrom pbapply pbsapply 
#' @importFrom zoo rollapply 
#'
#' @examples
#' dates <- as.Date("2019-01-01")+0:729
#' x <- rgamma(n = 730, shape = 0.4, rate = 0.5)
#' df <- data.frame(date=dates,RR=x)
#' IDF.agg(list(df),ds=c(24,48))
#' 
#'##        xdat ds station
#'## 1 0.3025660 24       1
#'## 2 0.4112304 24       1
#'## 3 0.1650978 48       1
#'## 4 0.2356849 48       1
IDF.agg <- function(data,ds,na.accept = 0,
                    which.stations = NULL,which.mon = 0:11,names = c('date','RR'),cl = NULL){
  
  if(!inherits(data, "list"))stop("Argument 'data' must be a list, instead it is a: ", class(data))
  
  # function 2: aggregate station data over durations and find annual maxima:                                
  agg.station <- function(station){
    data.s <- data[[station]]
    if(!is.data.frame(data.s)){stop("Elements of 'data' must be data.frames. But element "
                                    ,station," contains: ", class(data.s))}
    if(sum(is.element(names[1:2],names(data.s)))!=2){stop('Dataframe of station ', station 
                                                          ,' does not contain $', names[1]
                                                          ,' or $', names[2], '.')}
    dtime<-as.numeric((data.s[,names[1]][2]-data.s[,names[1]][1]),units="hours")
    
    if(any(ds %% dtime != 0)){
      stop('Given aggragation durations are not multiples of the time resolution = '
           ,dtime,' of station ',station,'.')}
    
    # function 1: aggregate over single durations and find annual maxima:
    agg.ts <- function(ds){
      runmean <- rollapply(data.s[,names[2]],ds/dtime,FUN=sum,fill =NA,align='right')
      runmean <- runmean/ds #intensity per hour
      subset <- is.element(as.POSIXlt(data.s[,names[1]])$mon,which.mon)
      max <- tapply(runmean[subset],(as.POSIXlt(data.s[,names[1]])$year+1900)[subset],
                    function(vec){
                      n.na <- sum(is.na(vec))
                      max <- ifelse(n.na <= na.accept,max(vec,na.rm = TRUE),NA)
                      return(max)})
      return(max) # maxima for single durations
    }
    # call function 1 in lapply to aggregate over all durations at single station
    data.agg <- pbsapply(ds,agg.ts,simplify=TRUE,cl=cl)  
    
    df <- data.frame(xdat = as.vector(data.agg), ds = rep(ds,each=length(data.agg[,1])))
    df$station <- rep(station,length(df$xdat))
    return(df) # maxima for all durations at one station
  }
  # which stations should be used?
  if(is.null(which.stations))which.stations <- 1:length(data)
  # call function 2 in lapply to aggregate over all durations at all stations
  station.list <- lapply(which.stations,agg.station)
  
  return(do.call('rbind',station.list))
}

#### IDF.plot ####



#' Plotting of IDF curves at a chosen station
#'
#' @param durations vector of durations for which to calculate the quantiles. 
#' @param fitparams vector containing parameters mut, sigma0, xi, theta, eta
#' (modified location, scale, shape, duration offset, duration exponent) for chosen station
#' as obtained from \code{\link{gev.d.fit}}
#' (or \code{\link{gev.d.params}} for model with covariates).
#' @param probs vector of exeedance probabilities for which to plot IDF curves (p = 1-1/ReturnPeriod)
#' @param cols vector of colors for IDF curves. Should have same length as \code{probs}
#' @param add logical indicating if plot should be added to existing plot
#' @param legend logical indicating if legend should be plotted
#' @param ... additional parameters passed on to the \code{plot} function
#'
#' @export
#' @importFrom grDevices rgb
#' @importFrom graphics axis box lines plot points 
#' @examples
#' data('example',package = 'IDF')
#' # fit d-gev
#' fit <- gev.d.fit(example$dat,example$d,ydat = as.matrix(example[,c("cov1","cov2")])
#'                  ,mul = c(1,2),sigl = 1)
#' # get parameters for cov1 = 1, cov2 = 1
#' par <- gev.d.params(fit = fit, ydat = matrix(1,1,2))
#' # plot quantiles
#' IDF.plot(durations = seq(0.5,35,0.2),fitparams = par)
#' # add data points
#' points(example[example$cov1==1,]$d,example[example$cov1==1,]$dat)
IDF.plot <- function(durations,fitparams,probs=c(0.5,0.9,0.99),
                     cols=4:2,add=FALSE,
                     legend=TRUE,...){
  
  # if cols is to short, make longer
  if(length(cols)!=length(probs))cols <- rep_len(cols,length.out=length(probs))
  
  ## calculate IDF values for given probability and durations
  idf.array <- simplify2array(qgev.d(probs,mut=fitparams[1],sigma0=fitparams[2],xi=fitparams[3],
                                     theta=fitparams[4],eta=fitparams[5],d=durations)) # array[probs,durs]
  if(!add){ #new plot
    ## initialize plot window
    # check if limits were passed
    if(is.element('ylim',names(list(...)))){
      ylim <- list(...)[['ylim']]
    }else{ylim <- range(idf.array,na.rm=TRUE)}
    if(is.element('ylim',names(list(...)))){
      xlim <- list(...)[['xlim']]
    }else{xlim <- range(durations,na.rm=TRUE)}
    
    # empty plot
    plot(NA,xlim=xlim,ylim=ylim,xlab="Duration [h]",ylab="Intensity [mm/h]",log="xy")
  }
  
  ## plot IDF curves
  for(i in 1:length(probs)){
    lines(durations,idf.array[i,],col=cols[i],...)
  }
  
  if(legend){## plot legend
    # check if lwd, lty were passed
    if(is.element('lwd',names(list(...)))){
      lwd <- list(...)[['lwd']]
    }else{lwd <- 1}
    if(is.element('lty',names(list(...)))){
      lty <- list(...)[['lty']]
    }else{lty <- 1}
    
    legend(x="topright",title = 'p-quantile',legend=probs,
           col=cols,lty=lty,lwd=lwd)
  }
}